Selection Criteria of Polyurethane Resins to Seal Concrete Joints in Underwater Road Tunnels in the Montreal Area
Bibliographic record
Abstract
Polyurethane resins are widely used in infrastructure repair technologies, particularly to stop water inflow in underground tunnels. However, the longevity of these chemical grouts is not well documented, as their application is relatively new. This is particularly true in Canada, and especially in the Montréal area, where tunnel walls are subject to many freezing and thawing cycles in one year, initiating some debonding and water inflow followed by ice formation. This presentation is dedicated to the characterization of various polyurethane resins under such environmental conditions. Special laboratory tests have been developed to measure the bonding strength and shrinkage of these resins injected into cracked concrete or in improperly sealed expansion joints. These traction tests and cyclic expansion/contraction tests illustrated the major role of environmental conditions (humidity and temperature) on the effectiveness of these resins. Several well-documented applications have been conducted during the rehabilitation of two major road tunnels in Montréal. The most effective imperviousness was obtained with a semi-rigid hydrophobic polyurethane resin, but it is also very important to specify how to prepare the fissures and how to inject these resins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".